├── m ├── 3.jpg ├── 4.jpg ├── demo.m └── cf_Xiao06.m ├── m_ruggedisation_update ├── 1.jpg ├── 2.jpg ├── Output.jpg ├── demo.m ├── cf_Xiao06_ruggedised.m └── MatchColumns.m ├── README.md └── LICENSE /m/3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hangong/Xiao06_color_transfer/HEAD/m/3.jpg -------------------------------------------------------------------------------- /m/4.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hangong/Xiao06_color_transfer/HEAD/m/4.jpg -------------------------------------------------------------------------------- /m_ruggedisation_update/1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hangong/Xiao06_color_transfer/HEAD/m_ruggedisation_update/1.jpg -------------------------------------------------------------------------------- /m_ruggedisation_update/2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hangong/Xiao06_color_transfer/HEAD/m_ruggedisation_update/2.jpg -------------------------------------------------------------------------------- /m_ruggedisation_update/Output.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hangong/Xiao06_color_transfer/HEAD/m_ruggedisation_update/Output.jpg -------------------------------------------------------------------------------- /m/demo.m: -------------------------------------------------------------------------------- 1 | % Demostration of Xiao's Image Colour Transfer 2 | 3 | % Copyright 2015 Han Gong , University of East 4 | % Anglia. 5 | 6 | % References: 7 | % Xiao, Xuezhong, and Lizhuang Ma. "Color transfer in correlated color 8 | % space." % In Proceedings of the 2006 ACM international conference on 9 | % Virtual reality continuum and its applications, pp. 305-309. ACM, 2006. 10 | 11 | I0 = im2double(imread('4.jpg')); 12 | I1 = im2double(imread('3.jpg')); 13 | 14 | IR = cf_Xiao06(I0,I1); 15 | 16 | figure; 17 | subplot(1,3,1); imshow(I0); title('Original Image'); axis off 18 | subplot(1,3,2); imshow(I1); title('Target Palette'); axis off 19 | subplot(1,3,3); imshow(IR); title('Result After Colour Transfer'); axis off 20 | -------------------------------------------------------------------------------- /m_ruggedisation_update/demo.m: -------------------------------------------------------------------------------- 1 | % Demonstration of Xiao's Image Colour Transfer 2 | 3 | % Modified Version with more robust processing. 4 | 5 | % Copyright 2015 Han Gong , University of East 6 | % Anglia. 7 | 8 | % With an addition by T E Johnson (April 2020) to ensure consistent 9 | % processing. 10 | 11 | % References: 12 | % Xiao, Xuezhong, and Lizhuang Ma. "Color transfer in correlated color 13 | % space." % In Proceedings of the 2006 ACM international conference on 14 | % Virtual reality continuum and its applications, pp. 305-309. ACM, 2006. 15 | 16 | I0 = im2double(imread('2.jpg')); 17 | I1 = im2double(imread('1.jpg')); 18 | 19 | % Process without additional ruggedisation processing. 20 | IR1 = cf_Xiao06_ruggedised(I0,I1,false); 21 | % Process with additional ruggedisation processing. 22 | IR2 = cf_Xiao06_ruggedised(I0,I1,true); 23 | 24 | figure; 25 | subplot(1,4,1); imshow(I0); title('Original Image'); axis off 26 | subplot(1,4,2); imshow(I1); title('Target Palette'); axis off 27 | subplot(1,4,3); imshow(IR1); title('Result from Standard Processing'); 28 | axis off 29 | subplot(1,4,4); imshow(IR2); title('Result from Ruggedised Processing'); 30 | axis off 31 | set(gcf, 'Position', get(0, 'Screensize')); 32 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Xiao06_color_transfer 2 | This is an open-source implementation of Xiao's image colour transfer algorithm published in 2006. MATLAB/Octave code is provided. 3 | 4 | 5 | Copyright 2015 Han Gong gong@fedoraproject.org, University of East Anglia 6 | 7 | 8 | Reference 9 | 10 | Xiao, Xuezhong, and Lizhuang Ma. "Color transfer in correlated color space." In Proceedings of the 2006 ACM international conference on Virtual reality continuum and its applications, pp. 305-309. ACM, 2006. 11 | 12 | MATLAB/Octave Code: see m/demo.m for demonstration. 13 | 14 | Results: 15 | 16 | ![Demo of Colour Transfer](http://cs.bath.ac.uk/~hg299/cf_Xiao06.png) 17 | # 18 | A further alternative implementation is provided under the directory *m_ruggedisation_update* in response to Issue #1. 19 | 20 | This incorporates additional code written and devised by T E Johnson. It enables an improved output image for input image pairs where the original processing method gave an unsatisfactory output. Note that as Johnson's alternative implementation is different from Xiao's paper, the improved 'ruggedisation' code should not be used for result comparisons if you are citing Xiao's paper. 21 | 22 | ![Demo of Colour Transfer Processing with Ruggedisation](m_ruggedisation_update/Output.jpg?raw=true) 23 | -------------------------------------------------------------------------------- /m/cf_Xiao06.m: -------------------------------------------------------------------------------- 1 | function est_im = cf_Xiao06(source,target) 2 | %CF_XIAO computes Reinhard's image colour transfer 3 | % 4 | % CF_XIAO(SOURCE,TARGET) returns the colour transfered source 5 | % image SOURCE according to the target image TARGET. 6 | % 7 | 8 | % References: 9 | % Xiao, Xuezhong, and Lizhuang Ma. "Color transfer in correlated color 10 | % space." % In Proceedings of the 2006 ACM international conference on 11 | % Virtual reality continuum and its applications, pp. 305-309. ACM, 2006. 12 | 13 | % TODO: Swatch-based transfer is not implemented (but I think it is not 14 | % important) Anyone interested is welcome to contribute. :-) 15 | % -- Han Gong 16 | 17 | % Copyright 2015 Han Gong , University of East 18 | % Anglia. 19 | 20 | rgb_s = reshape(im2double(source),[],3)'; 21 | rgb_t = reshape(im2double(target),[],3)'; 22 | 23 | % compute mean 24 | mean_s = mean(rgb_s,2); 25 | mean_t = mean(rgb_t,2); 26 | 27 | % compute covariance 28 | cov_s = cov(rgb_s'); 29 | cov_t = cov(rgb_t'); 30 | 31 | % decompose covariances 32 | [U_s,A_s,~] = svd(cov_s); 33 | [U_t,A_t,~] = svd(cov_t); 34 | 35 | rgbh_s = [rgb_s;ones(1,size(rgb_s,2))]; 36 | 37 | % compute transforms 38 | % translations 39 | T_t = eye(4); T_t(1:3,4) = mean_t; 40 | T_s = eye(4); T_s(1:3,4) = -mean_s; 41 | % rotations 42 | R_t = blkdiag(U_t,1); R_s = blkdiag(inv(U_s),1); 43 | % scalings 44 | % NOTE! 45 | % I believe the author has a typo in his original paper 46 | % The following is the original way to compute S_t, but 47 | % the result does not seem correct 48 | % S_t = diag([diag(A_t);1]); 49 | % I added a 0.5 power to correct it. 50 | S_t = diag([diag(A_t).^(0.5);1]); 51 | S_s = diag([diag(A_s).^(-0.5);1]); 52 | 53 | rgbh_e = T_t*R_t*S_t*S_s*R_s*T_s*rgbh_s; % estimated RGBs 54 | rgbh_e = bsxfun(@rdivide, rgbh_e, rgbh_e(4,:)); 55 | rgb_e = rgbh_e(1:3,:); 56 | 57 | est_im = reshape(rgb_e',size(source)); 58 | 59 | end 60 | -------------------------------------------------------------------------------- /m_ruggedisation_update/cf_Xiao06_ruggedised.m: -------------------------------------------------------------------------------- 1 | function est_im = cf_Xiao06_ruggedised(source,target,ruggedised) 2 | %CF_XIAO computes Reinhard's image colour transfer 3 | % 4 | % CF_XIAO(SOURCE,TARGET,ruggedised) returns the colour transfered source 5 | % image SOURCE according to the target image TARGET. 6 | % 7 | 8 | % If 'ruggedised' is set 'false' then, the original 'Xiao' method is 9 | % implemented as coded by Han Gong. 10 | 11 | % If 'ruggedised' is set 'true' then further processing is performed in 12 | % addition to the basic processing. The additional processing has been 13 | % devised and coded by Terry Johnson. 14 | 15 | % References: 16 | % Xiao, Xuezhong, and Lizhuang Ma. "Color transfer in correlated color 17 | % space." % In Proceedings of the 2006 ACM international conference on 18 | % Virtual reality continuum and its applications, pp. 305-309. ACM, 2006. 19 | 20 | % TODO: Swatch-based transfer is not implemented (but I think it is not 21 | % important) Anyone interested is welcome to contribute. :-) 22 | % -- Han Gong 23 | 24 | % Copyright 2015 Han Gong , University of East 25 | % Anglia. 26 | 27 | rgb_s = reshape(im2double(source),[],3)'; 28 | rgb_t = reshape(im2double(target),[],3)'; 29 | 30 | % compute mean 31 | mean_s = mean(rgb_s,2); 32 | mean_t = mean(rgb_t,2); 33 | 34 | % compute covariance 35 | cov_s = cov(rgb_s'); 36 | cov_t = cov(rgb_t'); 37 | 38 | % decompose covariances 39 | [U_s,A_s,~] = svd(cov_s); 40 | [U_t,A_t,~] = svd(cov_t); 41 | 42 | %********************************************************** 43 | % Processing modification Terry Johnson. 44 | if (ruggedised) 45 | [U_t,A_t]=MatchColumns(U_s,U_t,A_t); 46 | end 47 | %********************************************************** 48 | 49 | rgbh_s = [rgb_s;ones(1,size(rgb_s,2))]; 50 | 51 | % compute transforms 52 | % translations 53 | T_t = eye(4); T_t(1:3,4) = mean_t; 54 | T_s = eye(4); T_s(1:3,4) = -mean_s; 55 | % rotations 56 | R_t = blkdiag(U_t,1); R_s = blkdiag(inv(U_s),1); 57 | % scalings 58 | % NOTE! 59 | % I believe the author has a typo in his original paper 60 | % The following is the original way to compute S_t, but 61 | % the result does not seem correct 62 | % S_t = diag([diag(A_t);1]); 63 | % I added a 0.5 power to correct it. 64 | S_t = diag([diag(A_t).^(0.5);1]); 65 | S_s = diag([diag(A_s).^(-0.5);1]); 66 | 67 | rgbh_e = T_t*R_t*S_t*S_s*R_s*T_s*rgbh_s; % estimated RGBs 68 | rgbh_e = bsxfun(@rdivide, rgbh_e, rgbh_e(4,:)); 69 | rgb_e = rgbh_e(1:3,:); 70 | 71 | est_im = reshape(rgb_e',size(source)); 72 | 73 | end 74 | -------------------------------------------------------------------------------- /m_ruggedisation_update/MatchColumns.m: -------------------------------------------------------------------------------- 1 | function [U_t,A_t] = MatchColumns(us,ut,at) 2 | % Ensures consistency between the target & source image rotation matrices. 3 | 4 | % This routine matches columns in the target image rotation matrix to those 5 | % in the source image rotation matrix. 6 | 7 | % Each rotation matrix is derived by undertaking a singular value 8 | % decomposition of the respective cross covariance matrices. The 9 | % outcome of the decomposition is ordered within the rotation matrix in 10 | % the order of the descending singular values. This often leads to 11 | % compatible rotation matrices but that is not guaranteed. Sometimes the 12 | % colour ordering of one rotation matrix may be different from the other. 13 | % 14 | % Additionally even when the matrices do correspond in orientation, they 15 | % need not correspond in direction. The direction axis rotation in one 16 | % matrix may be the negative of the rotation in the other. 17 | 18 | % Rotations of the individual colour axes are given by the columns of the 19 | % rotation matrices. In the following processing, all rearrangements are 20 | % considered of the columns in the target rotation matrix 'ut', to find 21 | % the arrangement that best matches the source rotation matrix 'us'. 22 | % Matching is measured by taking the vector dot products of the 23 | % corresponding matrix columns and finding the arrangement with the 24 | % largest sum of absolute dot product values. Absolute values are used 25 | % to accommodate axis pairs that have similar orientations but different 26 | % directions. 27 | % 28 | % Once the best match has been found this is taken as the correct target 29 | % image rotation matrix. Columns are negated where the vector cross 30 | % product has a negative value, to ensure direction compatibility. The 31 | % singular value matrix for the target image is reordered to match the 32 | % reordering of the rotation matrix. 33 | % 34 | % This processing method and routine copyright Dr T E Johnson 2020. 35 | % terence.johnson@gmail.com 36 | 37 | % All 6 permutations of the three columns need to be addressed. 38 | perm = perms([1 2 3]); 39 | 40 | max=0.0; 41 | 42 | for i=1:6 43 | % Compute the sum of the absolute dot products for the matrix columns. 44 | sum=abs(ut(:,perm(i,1)).'*us(:,1)) + ... 45 | abs(ut(:,perm(i,2)).'*us(:,2)) + ... 46 | abs(ut(:,perm(i,3)).'*us(:,3)) ; 47 | 48 | if(sum>max) 49 | % Best aligned axes give the biggest sum. 50 | max=sum; 51 | bestperm=i; 52 | end 53 | end 54 | % Now recover the best permutation and implement it. 55 | b1=perm(bestperm,1); 56 | b2=perm(bestperm,2); 57 | b3=perm(bestperm,3); 58 | 59 | % Set the rotation matrix columns to the new order, changing the 60 | % direction of rotations if necessary by negating column values. 61 | U_t(:,1)=ut(:,b1)*sign(ut(:,b1).'*us(:,1)); 62 | U_t(:,2)=ut(:,b2)*sign(ut(:,b2).'*us(:,2)); 63 | U_t(:,3)=ut(:,b3)*sign(ut(:,b3).'*us(:,3)); 64 | 65 | % Similarly reorder the singular values. 66 | A_t(1,1)=at(b1,b1); 67 | A_t(2,2)=at(b2,b2); 68 | A_t(3,3)=at(b3,b3); 69 | end 70 | 71 | 72 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU LESSER GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | 9 | This version of the GNU Lesser General Public License incorporates 10 | the terms and conditions of version 3 of the GNU General Public 11 | License, supplemented by the additional permissions listed below. 12 | 13 | 0. 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